{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def evaluation(path):\n",
    "    y_prediction = np.loadtxt(path+'pred.csv')\n",
    "    y_real = np.loadtxt(path+'real.csv')\n",
    "    y_score = np.loadtxt(path+'prob.csv')\n",
    "    print('acc:',metrics.accuracy_score(y_true=y_real,y_pred=y_prediction))\n",
    "    print('f1:',metrics.f1_score(y_real,y_prediction))\n",
    "    print('confusion_matrix:',metrics.confusion_matrix(y_real,y_prediction))\n",
    "    print('recall:',metrics.recall_score(y_real, y_prediction))\n",
    "    fpr,tpr,threshold = metrics.roc_curve(y_real, y_score, pos_label=1) ###计算真正率和假正率\n",
    "    roc_auc = metrics.auc(fpr,tpr) ###计算auc\n",
    "    print(\"roc:\",roc_auc)\n",
    "    lw = 2\n",
    "    plt.figure(figsize=(10,10))\n",
    "    plt.plot(fpr, tpr, color='darkorange',\n",
    "             lw=lw, label='ROC curve (area = %0.2f)' % roc_auc) ###假正率为横坐标，真正率为纵坐标做曲线\n",
    "    plt.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--')\n",
    "    plt.xlim([0.0, 1.0])\n",
    "    plt.ylim([0.0, 1.05])\n",
    "    plt.xlabel('False Positive Rate')\n",
    "    plt.ylabel('True Positive Rate')\n",
    "    plt.title('Receiver operating characteristic example')\n",
    "    plt.legend(loc=\"lower right\")\n",
    "    plt.savefig(path+'roc.png', format='png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "acc: 0.859375\n",
      "f1: 0.7906976744186047\n",
      "confusion_matrix: [[38  1]\n",
      " [ 8 17]]\n",
      "recall: 0.68\n",
      "roc: 0.4707692307692308\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "evaluation('./result_2/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "pytorch",
   "language": "python",
   "name": "pytorch"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
